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arXiv · 2302.11594

L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Sascha Diefenbacher·Engin Eren·Frank Gaede·Gregor Kasieczka·Claudius Krause·Imahn Shekhzadeh·David Shih
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Abstract

We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the International Large Detector (ILD). Our proposed method -- which we refer to as "Layer-to-Layer-Flows" (L$2$LFlows) -- is an evolution of the CaloFlow architecture adapted to a higher-dimensional setting (30 layers of $10\times 10$ voxels each). The main innovation of L$2$LFlows consists of introducing $30$ separate normalizing flows, one for each layer of the calorimeter, where each flow is conditioned on the previous five layers in order to learn the layer-to-layer correlations. We compare our results to the BIB-AE, a state-of-the-art generative network trained on the same dataset and find our model has a significantly improved fidelity.

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Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Claudius Krause, Imahn Shekhzadeh, David Shih. 2023-02-22. L2LFlows: Generating High-Fidelity 3D Calorimeter Images. https://doi.org/10.1088/1748-0221%2F18%2F10%2Fp10017

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